A few years ago, Artificial Intelligence (AI), was in its experimental stage but now, this has changed. Surveys clearly exhibit that over 80% of all organizations of some size are investing in AI in some form or the other. AI has become prevalent across functions including marketing. This is indeed a very exciting phase for marketers who are now witnessing massive automation in digital marketing. Automation is based on decision rules (algorithm) that marketers set up and run for things like programmatic ad buying. It is also pretty common nowadays to read reports about how start-ups claim that AI systems can write much better emails, Facebook posts or creative copy than expert human copywriters.
AI encompasses any technology that seeks to mimic human intelligence, which covers a huge range of capabilities such as voice and image recognition, machine learning techniques and semantic search. However, the real value for organisations lie in the ability to implement these technologies to solve real problems. Today, there are AI tools for virtually every facet of marketing, making the function far more effective and efficient.
So here are some of the ways AI makes a true difference to a marketer:
- Virtual personal assistants: Basic tasks such as emails management, scheduling to optimized social media posting and cognitive bots for personalisation.
- To drive better data driven decisions: This is done through omni-channel data management platforms, forecasting media buying mix, real-time content recommendations, social media analytics
- Organizing and analysing visual assets: image classification, optimizing ad imagery
- Creating content: NLP platforms can be leveraged for for intelligent suggestions, automated campaigns and conversations with potential buys as well as comprehensive multi-purpose tools offering omni-channel marketing intelligence and automated targeting and optimization in media buying.
AI in customer life cycle: Another facet of marketing is customer life cycle where AI has proved its mettle. Starting with prospecting to focused customer targeting to converting them to sales and finally to a process of continuous engagement. So AI today can actually be seen across all these 4 stages. In the reaching or prospecting stage, AI helps to build propensity models to attract the right customers at a lower cost. Also, AI generated content is also useful, either for routine tasks that are recurring in nature where humans can be replaced, or in niche applications as part of content marketing strategy. Another key use case which is being experimented with and will likely see success is voice search. This will impact SEO strategies in future, as AI driven personal assistants will lead to increased voice traffic.
Predictive analytics is currently widely used with a wide range of behavioural and transactional variables. The success of these techniques to make accurate predictions will of course depend on the robustness of the underlying data. Further machine learning techniques can also be used to run through large data sets to predict the most effective content and ad placement. This has the potential to increase both productivity and efficiency in ad targeting. Propensity models can also be used to predict more accurately the stage in the buying process and personalise content which is far more relevant. Consumers want to be empowered by content that is specific to their needs and interests, and tolerance for irrelevant advertisements is at an all-time low personalisation in turn serves to increase conversion rates from prospects to customers. Then again machine learning can also be used to identify content that is most likely to bring customers back to your brand.
Optimising re-targeting ads to make them more effective is a good use case. Also, the use of chat bots for aiding customers with search and zoning in to the right offering has become fairly common. Bots today are able to respond to almost all common queries and further success will be based on the ability to constantly enhance learning.
AI is here to stay
Lastly, true cost savings and effectiveness will also come from using AI techniques in the customer engagement journey as a continuous process. Machine Learning tools can help establish most effective contact times, messages, triggers and much more. these can work to substantially enhance the effectiveness of marketing efforts.
Having said all this, for AI efforts to work better in any function, including advertising and marketing, it requires deeper understanding amongst the people using it. This will require up-skilling on internal teams to enable them to use the technology available more effectively. Automation probably works better than the technology has an advantage over humans. So the idea should be to use human time for strategic tasks and highly creative processes, and use AI techniques for the rest.
The author is Chief Marketing and Digital officer at Tata Capital
Disclaimer: The views expressed here are solely those of the author and do not in any way represent the views of exchange4media.com
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